Enhancing Lung Disease Diagnosis Through A Hybrid Deep Learning Approach Pro Chest X-Ray Image Classification - K. A., Sreeja; M., Arshey; Warrier, Gayathry S.; Pradeep, Arun (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Lung disorders have a wide-reaching influence, resulting in reduced lung function and a variety of complications, such as breathing difficulty, airway blockages, and exhalation problems. Due to limited resources for lab tests and imaging procedures, early diagnosis of illnesses such as pneumonia, fibrosis, etc., remains difficult. The use of chest X-ray pictures for quick disease monitoring, crucial for ICU patients, has gained popularity due to this problem, and image processing & machine learning models have become more popular. Deep learning for lung disease detection entails three critical steps: picture pre-processing, training, and classification. Relevant features indicative of lung disorders are extracted utilizing a range of deep learning methodologies such as CNNs, RNNs, Attention Mechanisms, Transfer Learning, GANs, and VGG architectures after improving the raw quality of X-ray images by optimal filtering techniques. While typical CNNs may struggle with complicated characteristics, potentially compromising lung cancer classification accuracy, a unique method has been developed via hybrid VGG-CNN architecture. This hybrid architecture captures local and global elements; whereas CNNs excel at detail-oriented aspects, VGG networks efficiently capture wider patterns. The effectiveness of this methodology is demonstrated using open datasets that include NIH Chest X-ray data. The classification of the gathered CNN features is so, therefore, performed using Random Forest and Support Vector Machine models. A variety of systems of measurement, such as accuracy, precision, recall, and F-measure, are used to evaluate this method’s effectiveness. The normal CNNVGG-SVM model’s accuracy of 93.54% is significantly outperformed by the hybrid SVM-RF model’s outstanding accuracy of 97.89%, a gain of 4.35%. Similarly, the hybrid RF model achieves an accuracy of 98.99%, outperforming the standard CNNVGG-RF model by 4.32%, or 94.89%. These metrics thoroughly evaluate the methodology’s capacity to diagnose various lung illnesses reliably. The effectiveness of the suggested technique is demonstrated by its exceptional accuracy in improving lung disease diagnosis.